To optimize a model during training, classification loss \(L_{cls}\)  is calculated from classification results with CrossEntropy loss, and regression loss \(L_{reg}\)  is calculated from regression results with SmoothL1 loss [1]}, defined as follows:
\(L_{cls} = \sum ^{N}_{i}\texttt {CrossEntropy}(\widehat{p_i}, y_i), \; L_{reg} = \sum ^{N}_{i}\texttt {SmoothL1}(\widehat{bbox_i}, bbox_i),\) 
